{"total":97,"rows":[{"id":"2305.08018","title":"DRew: Dynamically Rewired Message Passing with Delay","created":"2023-05-13","cited_by_count":82,"area":null},{"id":"2310.01668","title":"Locality-Aware Graph-Rewiring in GNNs","created":"2023-10-02","cited_by_count":56,"area":null},{"id":"2306.03589","title":"How does over-squashing affect the power of GNNs?","created":"2023-06-06","cited_by_count":53,"area":null},{"id":"2502.10818","title":"On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning","created":"2025-02-15","cited_by_count":43,"area":null},{"id":"2502.17598","title":"Hallucination Detection in LLMs Using Spectral Features of Attention Maps","created":"2025-02-24","cited_by_count":43,"area":null},{"id":"2309.09384","title":"Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature","created":"2023-09-17","cited_by_count":41,"area":null},{"id":"2404.04612","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","created":"2024-04-06","cited_by_count":24,"area":null},{"id":"2308.15568","title":"Over-Squashing in Graph Neural Networks: A Comprehensive survey","created":"2023-08-29","cited_by_count":23,"area":null},{"id":"2311.07073","title":"Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges","created":"2023-11-13","cited_by_count":22,"area":null},{"id":"2410.03424","title":"Cayley Graph Propagation","created":"2024-10-04","cited_by_count":21,"area":null},{"id":"2505.15547","title":"Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning","created":"2025-05-21","cited_by_count":19,"area":null},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2405.13526","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","created":"2024-05-22","cited_by_count":14,"area":null},{"id":"2405.15540","title":"Bundle Neural Networks for message diffusion on graphs","created":"2024-05-24","cited_by_count":14,"area":null},{"id":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2502.02504","title":"Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2502.04891","title":"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring","created":"2025-02-07","cited_by_count":13,"area":null},{"id":"2408.07191","title":"Joint Graph Rewiring and Feature Denoising via Spectral Resonance","created":"2024-08-13","cited_by_count":12,"area":null},{"id":"2506.07624","title":"Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks","created":"2025-06-09","cited_by_count":12,"area":null},{"id":"2407.09381","title":"The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited","created":"2024-07-12","cited_by_count":11,"area":null},{"id":"2506.05971","title":"On Measuring Long-Range Interactions in Graph Neural Networks","created":"2025-06-06","cited_by_count":11,"area":null},{"id":"2406.03671","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2411.17429","title":"Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey","created":"2024-11-26","cited_by_count":8,"area":null},{"id":"2506.16110","title":"Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification","created":"2025-06-19","cited_by_count":8,"area":null},{"id":"2309.09517","title":"FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks","created":"2023-09-18","cited_by_count":7,"area":null},{"id":"2310.07430","title":"Non-backtracking Graph Neural Networks","created":"2023-10-11","cited_by_count":7,"area":null},{"id":"2510.17457","title":"Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models","created":"2025-10-20","cited_by_count":7,"area":null},{"id":"2401.14580","title":"Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2504.04052","title":"PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networks","created":"2025-04-05","cited_by_count":6,"area":null},{"id":"2403.11199","title":"Graph Unitary Message Passing","created":"2024-03-17","cited_by_count":5,"area":null},{"id":"2305.03223","title":"Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance","created":"2023-05-05","cited_by_count":4,"area":null},{"id":"2310.04190","title":"On the Two Sides of Redundancy in Graph Neural Networks","created":"2023-10-06","cited_by_count":4,"area":null},{"id":"2508.19071","title":"Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks","created":"2025-08-26","cited_by_count":4,"area":null},{"id":"2511.04557","title":"Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning","created":"2025-11-06","cited_by_count":4,"area":null},{"id":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2408.01018","title":"GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs","created":"2024-08-02","cited_by_count":3,"area":null},{"id":"2410.05593","title":"When Graph Neural Networks Meet Dynamic Mode Decomposition","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2511.13010","title":"Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2511.20406","title":"Short-Range Oversquashing","created":"2025-11-25","cited_by_count":3,"area":null},{"id":"2305.19717","title":"An Empirical Evaluation of Rewiring Approaches in Graph Neural Networks","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2310.15197","title":"Can strong structural encoding reduce the importance of Message Passing?","created":"2023-10-22","cited_by_count":2,"area":null},{"id":"2405.16185","title":"Differentiable Cluster Graph Neural Network","created":"2024-05-25","cited_by_count":2,"area":null},{"id":"2407.05649","title":"Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2407.18480","title":"Scalable Graph Compressed Convolutions","created":"2024-07-26","cited_by_count":2,"area":null},{"id":"2503.00547","title":"Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing","created":"2025-03-01","cited_by_count":2,"area":null},{"id":"2506.06582","title":"Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2506.15708","title":"Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2509.22259","title":"Rotary Position Encodings for Graphs","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2311.15945","title":"Over-Squashing in Riemannian Graph Neural Networks","created":"2023-11-27","cited_by_count":1,"area":null},{"id":"2405.11968","title":"Conditional Shift-Robust Conformal Prediction for Graph Neural Network","created":"2024-05-20","cited_by_count":1,"area":null},{"id":"2410.05536","title":"Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2410.16822","title":"Can Large Language Models Act as Ensembler for Multi-GNNs?","created":"2024-10-22","cited_by_count":1,"area":null},{"id":"2502.07364","title":"Effects of Dropout on Performance in Long-range Graph Learning Tasks","created":"2025-02-11","cited_by_count":1,"area":null},{"id":"2508.05070","title":"TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows","created":"2025-08-07","cited_by_count":1,"area":null},{"id":"2508.09265","title":"Over-Squashing in GNNs and Causal Inference of Rewiring Strategies","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2508.20597","title":"Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks","created":"2025-08-28","cited_by_count":1,"area":null},{"id":"2509.12530","title":"Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2510.08450","title":"gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2510.20556","title":"Structural Invariance Matters: Rethinking Graph Rewiring through Graph Metrics","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2511.12709","title":"Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations","created":"2025-11-16","cited_by_count":1,"area":null},{"id":"2511.16062","title":"Gauge-Equivariant Graph Networks via Self-Interference Cancellation","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2512.09182","title":"Understanding the Failure Modes of Transformers through the Lens of Graph Neural Networks","created":"2025-12-09","cited_by_count":1,"area":null},{"id":"2602.07256","title":"Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2603.11944","title":"Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2306.04828","title":"Fast and Effective GNN Training through Sequences of Random Path Graphs","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2401.01233","title":"GEN: A Practical Alternative to Graph Transformers for Long-Range Graph Modeling","created":"2024-01-02","cited_by_count":0,"area":null},{"id":"2402.06908","title":"Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2406.07574","title":"Biharmonic Distance of Graphs and its Higher-Order Variants: Theoretical Properties with Applications to Centrality and Clustering","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2407.11756","title":"A Theoretical Formulation of Many-body Message Passing Neural Networks","created":"2024-07-16","cited_by_count":0,"area":null},{"id":"2410.09867","title":"Towards characterizing the value of edge embeddings in Graph Neural Networks","created":"2024-10-13","cited_by_count":0,"area":null},{"id":"2412.19419","title":"Introduction to Graph Neural Networks for Machine Learning Engineers","created":"2024-12-27","cited_by_count":0,"area":null},{"id":"2501.06002","title":"DeltaGNN: Graph Neural Network with Information Flow Control","created":"2025-01-10","cited_by_count":0,"area":null},{"id":"2502.06126","title":"Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2505.10392","title":"Schreier-Coset Graph Propagation","created":"2025-05-15","cited_by_count":0,"area":null},{"id":"2508.11390","title":"A Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Lifting","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2509.06777","title":"Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2509.10337","title":"Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2601.11159","title":"Theoretically and Practically Efficient Resistance Distance Computation on Large Graphs","created":"2026-01-16","cited_by_count":0,"area":null},{"id":"2601.14522","title":"On the Runway Cascade of Transformers for Language Modeling","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2602.10031","title":"Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2602.21092","title":"Probing Graph Neural Network Activation Patterns Through Graph Topology","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.27529","title":"Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs","created":"2026-03-29","cited_by_count":0,"area":null},{"id":"2605.00951","title":"Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.07577","title":"Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.09457","title":"RAwR: Role-Aware Rewiring via Approximate Equitable Partition","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2605.13383","title":"Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.23467","title":"S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.03068","title":"Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.07338","title":"VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2606.21333","title":"Ramanujan Graph Rewiring with Non Negative Resistance Curvature","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.27479","title":"Schreier-Coset Graph Rewiring","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.10619","title":"Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.11431","title":"AutoGrable: What Is a Good Graph for a Table?","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2610.02175","title":"Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.03556","title":"Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.04157","title":"Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective","created":"2026-10-03","cited_by_count":0,"area":null},{"id":"2610.16349","title":"RIFT: Token Influence Follows the Path of Least Resistance","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}